Risk homozygous haplotype regions for autism identifies population-specific ten genes for numerous pathways

Background Recessive homozygous haplotype (rHH) mapping is a reliable tool for identifying recessive genes by detecting homozygous segments of identical haplotype structures. These are shared at a higher frequency amongst probands compared to parental controls. Finding out such rHH blocks in autism subjects can help in deciphering the disorder etiology. Objectives The study aims to detect rHH segments of identical haplotype structure shared at a higher frequency in autism subjects than controls to identify recessive genes responsible for autism manifestation. Methods In the present study, 426 unrelated autism genotyped probands with 232 parents (116 trios) were obtained from Gene Expression Omnibus (GEO) Database. Homozygosity mapping analyses have been performed on the samples using standardized algorithms using the Affymetrix GeneChip® 500K SNP Nsp and Sty mapping arrays datasets. Results A total of 38 homozygous haplotype blocks were revealed across sample datasets. Upon downstream analysis, 10 autism genes were identified based on selected autism candidate genes criteria. Further, expressive Quantitative Trait Loci (QTL) analysis of SNPs revealed various binding sites for regulatory proteins BX3, FOS, BACH1, MYC, JUND, MAFK, POU2F2, RBBP5, RUNX3, and SMARCA4 impairing essential autism genes CEP290, KITLG, CHD8, and INS2. Pathways and processes such as adherens junction, dipeptidase activity, and platelet-derived growth factor—vital to autism manifestation were identified with varied protein-protein clustered interactions. Conclusion These findings bring various population clusters with significant rHH genes. It is suggestive of the existence of common but population-specific risk alleles in related autism subjects.

Abstract 14839: High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation

Introduction: Spatiotemporal differences in atrial activity are thought to contribute to the maintenance of atrial fibrillation (AF). While recent evidence has identified changes in dominant frequency (DF) during the transition from paroxysmal to persistent AF, little is known about the frequency characteristics of the epicardium during this transition. The purpose of this study was to perform high-resolution mapping of the atrial epicardium and to characterize changes in frequency activity and structural organization during the transition from paroxysmal to persistent AF. Hypothesis: In a porcine model of persistent AF, we tested the hypothesis that the epicardium undergoes spatiotemporal changes in atrial activity and structural organization during persistent AF. Methods: Paroxysmal and persistent AF was induced in adult Yorkshire swine by atrial tachypacing. Atrial morphology was segmented from magnetic resonance imaging and high-resolution patient-specific flexible mapping arrays were 3D printed to match the epicardial contours of the atria. Epicardial activation and DF mapping was performed in four paroxysmal and four persistent AF animals using personalized mapping arrays. Histological analysis was performed to determine structural differences between paroxysmal and persistent AF. Results: The left atrial epicardium was associated with a significant increase in DF between paroxysmal and persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). High-resolution spatiotemporal mapping identified organized clusters of DF during paroxysmal AF which were lost during persistent AF. The development of persistent AF led to structural remodeling with increased atrial epicardial fibrosis. The organization index (OI) significantly decreased during persistent AF in both the left atria (0.3 ± 0.03 vs. 0.2 ± 0.03, P = 0.01) and right atria (0.33 ± 0.04 vs. 0.23 ± 0.02, P = 0.02). Conclusions: In the porcine model of persistent AF, the epicardium undergoes structural remodeling with increased epicardial fibrosis, reflected by changes in atrial organization index and dominant frequency.

Abstract 15818: Three-dimensional Electroanatomic Mapping of Human Ventricular Tachycardia

Introduction: The localization of sources responsible for erratic ventricular tachycardia signaling can be facilitated by high-resolution epicardial mapping. Here, we describe an approach for epicardial mapping using 3D printed electrode mapping arrays. We utilize these mapping arrays to perform global beat-to-beat epicardial mapping of ex vivo Langendorff perfused human hearts and demonstrate the ability to identify sources of ventricular tachycardia. Hypothesis: The identification of ventricular tachycardia sources can be facilitated by epicardial mapping with 3D printed flexible mapping arrays. Methods: Epicardial shells were printed using a stereolithography 3D printer using flexible photopolymer resin followed by coupling with custom-designed flexible electrode arrays. Global electroanatomic maps were obtained from Langendorff perfused human hearts during sinus rhythm and after extra stimulus pacing induced ventricular tachycardia. Results: We demonstrate the use of 3D-printed electrode mapping arrays to perform global electroanatomic mapping of the human ventricular epicardium. Flexible 3D printed electrode arrays facilitate high spatial-temporal capture of electrophysiological data in Langendorff perfused human hearts and are capable of identifying epicardial sites of ventricular tachycardia. Conclusions: We report on flexible electrode mapping arrays shaped to match the epicardial contours of the human left and right ventricle. We demonstrate the ability to perform high spatiotemporal resolution mapping of human epicardial signals and demonstrate beat-to-beat global capture of ventricular tachycardia.

Megaflashes: Just How Long Can a Lightning Discharge Get?

The existence of mesoscale lightning discharges on the order of 100 km in length has been known since the radar-based findings of Ligda in the mid-1950s. However, it took the discovery of sprites in 1989 to direct significant attention to horizontally extensive “megaflashes” within mesoscale convective systems (MCSs). More recently, 3D Lightning Mapping Arrays (LMAs) have documented sprite-initiating lightning discharges traversing several hundred kilometers. One such event in a 2007 Oklahoma MCS having an LMA-derived length of 321 km, has been certified by the WMO as the longest officially documented lightning flash. The new Geostationary Lightning Mapper (GLM) sensor on GOES-16/17 now provides an additional tool suited to investigating mesoscale lightning. On 22 October 2017, a quasi-linear convective system moved through the central United States. At 0513 UTC, the GLM indicated a lightning discharge originated in northern Texas, propagated north-northeast across Oklahoma, fortuitously traversed the Oklahoma LMA (OKLMA), and finally terminated in southeastern Kansas. This event is explored using the OKLMA, the National Lightning Detection Network (NLDN), and the GLM. The NLDN reported 17 positive cloud-to-ground flashes (+CGs), 23 negative CGs (−CGs), and 37 intracloud flashes (ICs) associated with this massive discharge, including two +CGs capable of inducing sprites, with others triggering upward lightning from tall towers. Combining all available data confirms the megaflash, which illuminated 67,845 km2, was at least 500 km long, greatly exceeding the current official record flash length. Yet even these values are being superseded as GLM data are further explored, revealing that such vast discharges may not be all that uncommon.

Two Methods for Correcting Range-Dependent Limitations of Lightning Mapping Arrays

Lightning Mapping Arrays (LMAs) detect very high frequency (VHF) radiation produced by lightning as it propagates; however, VHF source detection efficiency drops off rapidly with range from the centers of the arrays, which results in a maximum of source points over the center of the network for large datasets. Using data from nearly one billion detected sources of various powers, an approximation of VHF source detection efficiency (relative to the number of sources detected within 25 km of the center of the array) for the Oklahoma LMA is calculated for different ranges and source powers. The calculated source detection efficiencies are then used to normalize the VHF source data out to a range of 125 km, as a method for correcting the detection efficiency drop-off with range. The data are also sorted into flashes using a popular flash-sorting algorithm in order to compare how well flash sorting corrects for detection efficiency drop-off with range compared to the normalization method. Both methods produce similar patterns and maxima of the lightning location, but the differences between them are identified and highlighted. The use of a flash-sorting algorithm is recommended for future studies involving large sets of data.